{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:30:14Z","timestamp":1760146214790,"version":"build-2065373602"},"reference-count":49,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T00:00:00Z","timestamp":1729036800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Office of Naval Research"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The purpose of this paper is to introduce a method of measuring spatiotemporal gait patterns, tibial accelerations, and heart rate that are matched with high resolution geographical terrain features using publicly available data. These methods were demonstrated using data from 218 Marines, who completed loaded outdoor ruck hikes between 5\u201320 km over varying terrain. Each participant was instrumented with two inertial measurement units (IMUs) and a GPS watch. Custom code synchronized accelerometer and positional data without a priori sensor synchronization, calibrated orientation of the IMUs in the tibial reference frame, detected and separated only periods of walking or running, and computed acceleration and spatiotemporal outcomes. GPS positional data were georeferenced with geographic information system (GIS) maps to extract terrain features such as slope, altitude, and surface conditions. This paper reveals the ease at which similar data can be gathered among relatively large groups of people with minimal setup and automated data processing. The methods described here can be adapted to other populations and similar ground-based activities such as skiing or trail running.<\/jats:p>","DOI":"10.3390\/s24206667","type":"journal-article","created":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T10:11:04Z","timestamp":1729073464000},"page":"6667","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Methods for Evaluating Tibial Accelerations and Spatiotemporal Gait Parameters during Unsupervised Outdoor Movement"],"prefix":"10.3390","volume":"24","author":[{"given":"Amy","family":"Silder","sequence":"first","affiliation":[{"name":"Naval Health Research Center, San Diego, CA 92106-3521, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ethan J.","family":"Wong","sequence":"additional","affiliation":[{"name":"Naval Health Research Center, San Diego, CA 92106-3521, USA"},{"name":"Leidos, Inc., La Jolla, CA 92152, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Brian","family":"Green","sequence":"additional","affiliation":[{"name":"Naval Health Research Center, San Diego, CA 92106-3521, USA"},{"name":"Leidos, Inc., La Jolla, CA 92152, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicole H.","family":"McCloughan","sequence":"additional","affiliation":[{"name":"Naval Health Research Center, San Diego, CA 92106-3521, USA"},{"name":"Leidos, Inc., La Jolla, CA 92152, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matthew C.","family":"Hoch","sequence":"additional","affiliation":[{"name":"College of Health Sciences, University of Kentucky, Lexington, KY 40506, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"102690","DOI":"10.1016\/j.humov.2020.102690","article-title":"Combining wearable sensor signals, machine learning and biomechanics to estimate tibial bone force and damage during running","volume":"74","author":"Matijevich","year":"2020","journal-title":"Hum. 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